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KINO: A Keyframe Interface for VLM Planning and Whole-Body Control in Humanoid Loco-Manipulation

arXiv:2609.18869v1 Announce Type: new Abstract: Humanoid loco-manipulation requires robots to interpret task instructions and scene semantics while executing coordinated whole-body motions. We propose

safetyarxiv-cs-ro

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Model Releases

Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering

DGX agent

arXiv:2609.18317v1 Announce Type: cross Abstract: Large language models (LLMs) suffer from a long-tail deficit: culturally specific facts, particularly those concerning underrepresented regions such a

model-releasesarxiv-cs-ai

17 Sept 2026

Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation

arXiv:2412.07255v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities in generative tasks but pose potential risks due to their tendency to generat

Label-free steering: Compressing test-time reinforcement learning into bias-only subspaces

model-releasesarxiv-cs-ai

arXiv:2609.18587v1 Announce Type: cross Abstract: Test-time reinforcement learning (TTRL) enables models to improve their reasoning without relying on labeled training data, but existing approaches ty

LangSelect: Cost-Aware Target-Language Routing for LLM Code Generation

model-releasesarxiv-cs-cl

arXiv:2609.18959v1 Announce Type: new Abstract: LLM code-generation systems usually choose a target programming language before decoding and treat that choice as fixed. We show that, for language-flex

Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation

arXiv:2609.17544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for clinical applications, yet their assessment for real-world traditional Chinese medicine

safetyarxiv-cs-cl

LaSeD: Label-Semantic Self-Distillation for Visual-Only Surgical Phase Recognition

arXiv:2609.18971v1 Announce Type: new Abstract: Surgical phase recognition maps each video frame to a clinically meaningful workflow phase, supporting context-aware assistance, documentation, and post

researcharxiv-cs-cv

Industry

Last Week in AI #344 - Navier–Stokes, Pacing the Frontier, AI Misuse

OpenAI announced that its internal GPT‑6 Astra–based agent system produced a proof of the Navier–Stokes existence and smoothness problem, claiming a solution to one of the Clay Millennium Prize proble

industrylast-week-in-ai

LEAP: Learning Emergent Active Perception for Quadruped Navigation

arXiv:2609.17628v1 Announce Type: new Abstract: Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to targe

agentsarxiv-cs-ro

Learning A Unified Template for Gait Recognition

arXiv:2609.18490v1 Announce Type: new Abstract: 'What I cannot create, I do not understand.'Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models ha

researcharxiv-cs-cv

Learning Array Signal Topologies as Conditional Neural Manifolds

model-releasesarxiv-cs-lg

arXiv:2609.18616v1 Announce Type: cross Abstract: Subspace methods such as multiple signal classification (MUSIC) achieve super-resolution direction of arrival (DoA) estimation by exploiting the ortho

Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion

arXiv:2509.12151v3 Announce Type: replace-cross Abstract: We present a learnable physics-based model that predicts motion of the robot end effector and reaction force-torque in contact-rich manipulati

agentsarxiv-cs-lg

Applications

Learning Fractional-Order Dynamics from a Single Trajectory

arXiv:2609.18127v1 Announce Type: new Abstract: Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the mo

applicationsarxiv-cs-lg

Learning from Distributed Eyes: Leveraging Collaborative Perception for Automated Model Adaptation

arXiv:2609.18511v1 Announce Type: cross Abstract: In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation of

agentsarxiv-cs-lg

Learning Heterogeneous Preferences

arXiv:2609.17847v1 Announce Type: new Abstract: Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in po

Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator

arXiv:2609.18930v1 Announce Type: new Abstract: Bipedal loco-manipulation enables robots to interact with objects beyond the nominal workspace of their arms by coordinating locomotion and manipulation

safetyarxiv-cs-ro

Learning Lyapunov Operators for Nonlinear Systems

arXiv:2609.18894v1 Announce Type: cross Abstract: Constructing Lyapunov functions for nonlinear dynamical systems is a central problem in stability analysis, yet remains challenging. Lyapunov function

researcharxiv-cs-lg

Learning Magnetic Order Classification from Large-Scale Materials Databases

arXiv:2509.05909v3 Announce Type: replace-cross Abstract: The reliable identification of magnetic ground states remains a major challenge in high-throughput materials databases, where density function

safetyarxiv-cs-lg

Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control

arXiv:2609.17824v1 Announce Type: cross Abstract: We study cooperative multi-humanoid pickup and transport of objects with varying size, weight, and geometry, requiring robot teams of different sizes.

tutorialsarxiv-cs-ai

Learning Nuclear Structure with AI: Radii and Collectivity

arXiv:2609.17838v1 Announce Type: cross Abstract: Low-energy nuclear structure is encoded in a broad body of experimental information across the chart of nuclides. Learning how this information is org

researcharxiv-cs-ai

Learning to Program Adaptive Non-Local Observables for Machine Learning

arXiv:2609.18655v1 Announce Type: new Abstract: Quantum neural networks (QNNs) are typically built from variational quantum circuits (VQCs), which are limited by local measurements. Adaptive non-local

tutorialsarxiv-cs-lg

Learning to Stack: Cube-Stacking Imitation Learning from Virtual Reality Demonstrations

arXiv:2609.19040v1 Announce Type: new Abstract: Imitation learning is attractive for robot manipulation, but collecting demonstrations remains a bottleneck for multi-stage tasks requiring repeated sce

safetyarxiv-cs-ro

Learning Where to Focus: Self-Supervised Multi-Scale ViTs for Histopathology

model-releasesarxiv-cs-cv

arXiv:2609.18578v1 Announce Type: new Abstract: Pathologists diagnose diseases by first locating suspicious tissue and then examining it at higher magnification, whereas self-supervised vision transfo

Hardware

Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications

arXiv:2609.17638v1 Announce Type: cross Abstract: This is the set of lecture notes for the PhD course href{https://www.unibz.it/en/faculties/engineering/phd-computer-science/study-course-offering/2025

hardwarearxiv-cs-ai

Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant

model-releasesarxiv-cs-cl

arXiv:2609.17546v1 Announce Type: new Abstract: In this position paper, we argue that legal LLMs' hallucinations should be evaluated as a failure of legal warrant rather than as factual inaccuracy or

Leveraging Complementary Embeddings for Replay Selection in Continual Learning with Small Buffers

arXiv:2604.08336v2 Announce Type: replace Abstract: Catastrophic forgetting remains a key challenge in Continual Learning (CL). In replay-based CL with severe memory constraints, performance criticall

researcharxiv-cs-lg

Lexara-RF: Reference-Free Metrics for Evaluating Conversational Visual Analytics Agents

arXiv:2609.17842v1 Announce Type: cross Abstract: Conversational visual analytics (CVA) agents powered by large language models generate visualizations and natural-language explanations from open-ende

LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era

arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally,

tutorialsarxiv-cs-lg

LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging

model-releasesarxiv-cs-lg

arXiv:2609.19062v1 Announce Type: new Abstract: Automatic sleep staging is fundamental to personal health monitoring, yet many existing approaches are ill-suited for real-world applications. Tradition

Limits of Transfer Learning

arXiv:2006.12694v2 Announce Type: replace-cross Abstract: Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely use

researcharxiv-cs-ai

Linguistic Triggers of Gender and Racial Bias in Open-Weight LLMs Applied to Recruitment

model-releasesarxiv-cs-ai

arXiv:2609.18106v1 Announce Type: cross Abstract: Open-weight large language models are rapidly entering hiring pipelines, yet their discriminatory failure modes -- and the regulatory exposure these c

LLMs lack theories of mind and body, are disembodied, possess neither homeostatic regulating feelings and emotions, are not persistent, and …

model-releasesgary-marcus--x

LLMs lack theories of mind and body, are disembodied, possess neither homeostatic regulating feelings and emotions, are not persistent, and cannot continuously learn or compute. As currently construct

Industry

LLMs respond differently to harmful prompts when AI watermarking is used

Artificial intelligence platforms are adopting AI watermarking—specifically Google’s open‑source SynthID‑Text—to meet new EU regulations, embedding a secret key that subtly changes word selection and

industryars-technica

LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition

model-releasesarxiv-cs-ai

arXiv:2510.08928v2 Announce Type: replace Abstract: Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce

Locating Hidden Failures Makes Long-Horizon Agents More Reliable

model-releasesarxiv-cs-lg

arXiv:2609.17930v1 Announce Type: new Abstract: As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether th

Loco-Loco-RL: Low-Cost Terrain Mapping for Humanoid Locomotion with Reinforcement Learning

model-releasesarxiv-cs-ro

arXiv:2609.19041v1 Announce Type: new Abstract: Informative terrain perception is important for robust reinforcement learning policies in humanoid locomotion. Still, common sensors such as depth camer

Local Ai

LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Edits

arXiv:2609.18720v1 Announce Type: new Abstract: Learned quality estimation (QE) models such as COMETKiwi are widespread and work well for general machine translation evaluation. However, they are know

local-aiarxiv-cs-cl

Applications

Long-Context Demonstration Selection Using State Space Models

arXiv:2609.17888v1 Announce Type: cross Abstract: We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This pro

applicationsarxiv-cs-cl

Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs

model-releasesarxiv-cs-cl

arXiv:2609.18935v1 Announce Type: new Abstract: A game character should not have to reread its entire life before every conversation. For locally deployed language-model characters, however, revising

Look Less, Hear Better: Jointly Rewarded GRPO for Streaming ASR

arXiv:2609.18333v1 Announce Type: cross Abstract: Streaming automatic speech recognition (ASR) must be judged jointly on what it transcribes and on how quickly it commits each word. Delayed streams mo

Lost in Perception: Isolating Perceptual and Reasoning Failures in Multimodal Physics and Geometry Reasoning

arXiv:2609.18991v1 Announce Type: new Abstract: Multimodal LLMs report strong performance on scientific reasoning benchmarks, yet most treat perception and reasoning as a single measurable process. We

researcharxiv-cs-ai

LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics

arXiv:2609.19039v1 Announce Type: cross Abstract: We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling,

researcharxiv-cs-lg

Lumen: Parameter-Efficient Alignment of Pretrained Vision and Language Encoders for Zero-Shot Computational Pathology

model-releasesarxiv-cs-cv

arXiv:2609.17868v1 Announce Type: new Abstract: Pathology vision-language models are commonly built by pretraining or fine-tuning large encoders on paired image-caption data. We asked whether a pathol

LUMO: Designing Luminous Contact Morphology for Repeatable Whole-Finger Contact Observation

arXiv:2609.18165v1 Announce Type: new Abstract: A low-impedance robot finger reports through joint torque how strongly it is loaded, but the same torque can arise from a small force near the fingertip

researcharxiv-cs-ro

M-SQE: Multilingual Skill Quality Estimation for Enhancing Language Equality in Agentic Skill Use

arXiv:2609.18445v1 Announce Type: new Abstract: Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agen

agentsarxiv-cs-cl

{M}^2Tok: Multi-head Multi-codebook Discrete Action Tokenization for Vision-Language-Action Models

arXiv:2609.18259v1 Announce Type: cross Abstract: Recent advancements have successfully adapted autoregressive language models to process multimodal signals, such as images and actions. Since raw acti

M^3P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation

arXiv:2609.18669v1 Announce Type: new Abstract: Multi-Modal Motion Planning (M^3P) requires joint reasoning over continuous motions and discrete mode transitions, making it difficult to solve efficien

researcharxiv-cs-ro

Machine learning kinetics from molecular dynamics data

arXiv:2609.17736v1 Announce Type: cross Abstract: Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration r

tutorialsarxiv-cs-lg